Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Khattak, Asad J.; Wali, Behram

doi:10.1016/j.trc.2017.08.004

Cited by 69 publications

(38 citation statements)

References 54 publications

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“…16 With the deepening of research, many kinds of prediction models have appeared. 16 With the deepening of research, many kinds of prediction models have appeared.…”

Section: Background and Motivationmentioning

confidence: 99%

“…The prediction process uses an iterative method, and the last line output data is as Equation 16: The prediction process uses an iterative method, and the last line output data is as Equation 16:…”

Section: Prediction Networkmentioning

confidence: 99%

“…We use well-trained LSTM networks (LSTM * net ) for prediction. The prediction process uses an iterative method, and the last line output data is as Equation 16:…”

Section: Prediction Networkmentioning

confidence: 99%

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Big data‐driven machine learning‐enabled traffic flow prediction

Kong

Jiang

et al. 2018

Trans Emerging Tel Tech

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Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. The key challenge of traffic flow prediction network is how to construct an adaptive model relying on historical data. Existing big data‐driven traffic flow prediction networking approaches mainly use shallow learning, and there are unsatisfying for many realistic applications, which inspire us to rethink the traffic flow big data prediction problem with deep learning. In this paper, we propose a novel prediction approach based on machine learning. In addition to the minimum prediction error as the goal, we present the long short‐term memory model, which is a typical machine learning algorithm with deep learning network. This method is applied into the real‐world traffic big data from performance measurement system. Experimental results show that the proposed machine learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network.

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“…16 With the deepening of research, many kinds of prediction models have appeared. 16 With the deepening of research, many kinds of prediction models have appeared.…”

Section: Background and Motivationmentioning

confidence: 99%

Section: Prediction Networkmentioning

confidence: 99%

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Big data‐driven machine learning‐enabled traffic flow prediction

Kong

Jiang

et al. 2018

Trans Emerging Tel Tech

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“…In CV environment, in-vehicle devices can receive some messages about surrounding traffic from surrounding vehicles or road infrastructures, these messages can assist drivers to drive safely and efficiently compared with conventional traffic. Some field experiments [14,15] indicated that drivers have shorter reaction time in CV environment. CV technologies can provide drivers much information about surrounding traffic, which have positive impact on car-following behaviour.…”

Section: Introductionmentioning

confidence: 99%

Traffic Oscillations Mitigation in Vehicle Platoon Using a Car-Following Control Model for Connected and Autonomous Vehicle

Gao

Zhang

Dong

et al. 2019

Journal of Advanced Transportation

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Traffic oscillations often occur in road traffic, they make traffic flow unstable, unsafe and inefficient. Emerging connected and autonomous vehicle (CAV) technologies are potential solutions to mitigating the traffic oscillations for the advantages that CAVs are controllable and cooperative. In order to study a control strategy and the effectiveness of CAVs in mitigating traffic oscillations and improving traffic flow and analyse the characteristics of homogeneous traffic flow made up of CAVs and heterogeneous traffic flow made up of CAVs and RVs when traffic oscillations appear in traffic flow. Firstly, the formation and propagation of traffic oscillations in a platoon of RVs are simulated and analysed. Then, a car-following control model is built to control the longitudinal motion of CAVs, and real-time information of preceding CAV is used in the model and this can make the motion of CAVs more cooperative. The model reflects an idea named “slow-in” and “fast-out,” and this idea is helpful to mitigate traffic oscillations. Then, numerical simulations of homogeneous traffic flow of a platoon of CAVs and simulations of heterogeneous traffic flow containing CAVs and RVs are conducted, and different penetration rates (0, 0.2, 0.4, 0.6, 0.8, and 1) of CAVs are considered in the simulations of heterogeneous traffic flow. The characteristics and evolution of traffic flow are analysed and some indexes reflecting traffic efficiency and stability are calculated and analysed. Simulation results show that there are smaller velocity fluctuation, less stopping time and shorter length of road occupied when vehicle platoon contains CAVs (penetration rates are from 0.2 to 1) compared to the platoon containing only RVs (without CAVs). As for the heterogeneous traffic flow containing CAVs and RVs, these three indexes decrease with the increase of penetration rates (from 0.2 to 1) of CAVs. These results indicate that CAVs with the car-following control model in vehicle platoon are beneficial for mitigating traffic oscillations and improving traffic flow.

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“…traffic volumes) can still vary significantly across similar, or even identical, road geometry and conditions within a jurisdiction (5). The correlations between crashes and associated factors can be heterogeneous, and it is important to correct for heterogeneity in modeled relationships that can arise from a number of observed and unobserved factors relating to (but not limited to) (5)(6)(7)(8)(9):  Driver behaviors  Vehicle types  Socioeconomic factors  Traffic and pavement characteristics  Road geometrics  Variations in police accident recording thresholds  Other time and space related unobserved factors. For a complete review of methodological challenges in crash frequency modeling, interested readers are referred to Lord & Mannering (10).…”

Section: Introductionmentioning

confidence: 99%

Development of Safety Performance Functions: Incorporating Unobserved Heterogeneity and Functional Form Analysis

Wali

Khattak

Waters

et al. 2018

Transportation Research Record: Journal of the Transportation R

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To improve transportation safety, this study applies Highway Safety Manual (HSM) procedures to roadways while accounting for unobserved heterogeneity and exploring alternative functional forms for Safety Performance Functions (SPFs). Specifically, several functional forms are considered in Poisson and Poisson-gamma modeling frameworks. Using five years (2011)(2012)(2013)(2014)(2015) of crash, traffic, and road inventory data for two-way, two-lane roads in Tennessee, fixed-and random-parameter count data models are calibrated. The models account for important methodological concerns of unobserved heterogeneity and omitted variable bias. With a validation dataset, the calibrated and uncalibrated HSM SPFs and eight new Tennessee-specific SPFs are compared for prediction accuracy. The results show that the statewide calibration factor is 2.48, suggesting rural two-lane, two-way road segment crashes are at least 1.48 times greater than what HSM SPF predicts. Significant variation in four different regions in Tennessee is observed with calibration factors ranging between 2.02 and 2.77. Among all the SPFs considered, fully specified Tennessee-specific random parameter Poisson SPF outperformed all competing SPFs in predicting out-ofsample crashes on these road segments. The best-fit random parameter SPF specification for crash frequency includes the following variables: annual average daily traffic, segment length, shoulder width, lane width, speed limit, and the presence of passing lanes. Significant heterogeneity is observed in the effects of traffic exposure-related variables on crash frequency. The study shows how heterogeneity-based models can be specified and used by practitioners for obtaining accurate crash predictions.

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Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Cited by 69 publications

References 54 publications

Big data‐driven machine learning‐enabled traffic flow prediction

Big data‐driven machine learning‐enabled traffic flow prediction

Traffic Oscillations Mitigation in Vehicle Platoon Using a Car-Following Control Model for Connected and Autonomous Vehicle

Development of Safety Performance Functions: Incorporating Unobserved Heterogeneity and Functional Form Analysis

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